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Fábregas-Ibáñez L, Jeschke G, Stoll S. Compactness regularization in the analysis of dipolar EPR spectroscopy data. JOURNAL OF MAGNETIC RESONANCE (SAN DIEGO, CALIF. : 1997) 2022; 339:107218. [PMID: 35439683 DOI: 10.1016/j.jmr.2022.107218] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/18/2022] [Revised: 04/04/2022] [Accepted: 04/05/2022] [Indexed: 06/14/2023]
Abstract
Dipolar electron paramagnetic resonance (EPR) experiments, such as double electron-electron resonance (DEER), measure distributions of nanometer-scale distances between paramagnetic centers, which are valuable for structural characterization of proteins and other macromolecular systems. One challenge in the least-squares fitting analysis of dipolar EPR data is the separation of the inter-molecular contribution (background) and the intra-molecular contribution. For noisy experimental traces of insufficient length, this separation is not unique, leading to identifiability problems for the background model parameters and the long-distance region of the intra-molecular distance distribution. Here, we introduce a regularization approach that mitigates this by including an additional penalty term in the objective function that is proportional to the variance of the distance distribution and thereby penalizes non-compact distributions. We examine the reliability of this approach statistically on a large set of synthetic data and illustrate it with an experimental example. The results show that the introduction of compactness can improve identifiability.
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Affiliation(s)
- Luis Fábregas-Ibáñez
- ETH Zurich, Laboratory of Physical Chemistry, Vladimir-Prelog-Weg 2, Zurich 8093, Switzerland.
| | - Gunnar Jeschke
- ETH Zurich, Laboratory of Physical Chemistry, Vladimir-Prelog-Weg 2, Zurich 8093, Switzerland
| | - Stefan Stoll
- University of Washington, Department of Chemistry, Seattle, WA 98195, USA
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Dray KE, Muldoon JJ, Mangan NM, Bagheri N, Leonard JN. GAMES: A Dynamic Model Development Workflow for Rigorous Characterization of Synthetic Genetic Systems. ACS Synth Biol 2022; 11:1009-1029. [PMID: 35023730 PMCID: PMC9097825 DOI: 10.1021/acssynbio.1c00528] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Mathematical modeling is invaluable for advancing understanding and design of synthetic biological systems. However, the model development process is complicated and often unintuitive, requiring iteration on various computational tasks and comparisons with experimental data. Ad hoc model development can pose a barrier to reproduction and critical analysis of the development process itself, reducing the potential impact and inhibiting further model development and collaboration. To help practitioners manage these challenges, we introduce the Generation and Analysis of Models for Exploring Synthetic Systems (GAMES) workflow, which includes both automated and human-in-the-loop processes. We systematically consider the process of developing dynamic models, including model formulation, parameter estimation, parameter identifiability, experimental design, model reduction, model refinement, and model selection. We demonstrate the workflow with a case study on a chemically responsive transcription factor. The generalizable workflow presented in this tutorial can enable biologists to more readily build and analyze models for various applications.
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Affiliation(s)
- Kate E. Dray
- Department of Chemical and Biological Engineering, Northwestern University, Evanston, IL 60208, USA
| | - Joseph J. Muldoon
- Department of Chemical and Biological Engineering, Northwestern University, Evanston, IL 60208, USA.,Interdisciplinary Biological Sciences Program, Northwestern University, Evanston, IL 60208, USA
| | - Niall M. Mangan
- Engineering Sciences and Applied Mathematics Program, Northwestern University, Evanston, IL 60208, USA.,Center for Synthetic Biology, Northwestern University, Evanston, IL 60208, USA
| | - Neda Bagheri
- Department of Chemical and Biological Engineering, Northwestern University, Evanston, IL 60208, USA.,Interdisciplinary Biological Sciences Program, Northwestern University, Evanston, IL 60208, USA.,Center for Synthetic Biology, Northwestern University, Evanston, IL 60208, USA.,Departments of Biology and Chemical Engineering, University of Washington, Seattle, WA 98195, USA.,Co-corresponding authors: Joshua N. Leonard, , Neda Bagheri,
| | - Joshua N. Leonard
- Department of Chemical and Biological Engineering, Northwestern University, Evanston, IL 60208, USA.,Interdisciplinary Biological Sciences Program, Northwestern University, Evanston, IL 60208, USA.,Center for Synthetic Biology, Northwestern University, Evanston, IL 60208, USA.,Chemistry of Life Processes Institute, and Robert H. Lurie Comprehensive Cancer Center, Northwestern University, Evanston, IL 60208, USA.,Co-corresponding authors: Joshua N. Leonard, , Neda Bagheri,
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Loskot P, Atitey K, Mihaylova L. Comprehensive Review of Models and Methods for Inferences in Bio-Chemical Reaction Networks. Front Genet 2019; 10:549. [PMID: 31258548 PMCID: PMC6588029 DOI: 10.3389/fgene.2019.00549] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2019] [Accepted: 05/24/2019] [Indexed: 01/30/2023] Open
Abstract
The key processes in biological and chemical systems are described by networks of chemical reactions. From molecular biology to biotechnology applications, computational models of reaction networks are used extensively to elucidate their non-linear dynamics. The model dynamics are crucially dependent on the parameter values which are often estimated from observations. Over the past decade, the interest in parameter and state estimation in models of (bio-) chemical reaction networks (BRNs) grew considerably. The related inference problems are also encountered in many other tasks including model calibration, discrimination, identifiability, and checking, and optimum experiment design, sensitivity analysis, and bifurcation analysis. The aim of this review paper is to examine the developments in literature to understand what BRN models are commonly used, and for what inference tasks and inference methods. The initial collection of about 700 documents concerning estimation problems in BRNs excluding books and textbooks in computational biology and chemistry were screened to select over 270 research papers and 20 graduate research theses. The paper selection was facilitated by text mining scripts to automate the search for relevant keywords and terms. The outcomes are presented in tables revealing the levels of interest in different inference tasks and methods for given models in the literature as well as the research trends are uncovered. Our findings indicate that many combinations of models, tasks and methods are still relatively unexplored, and there are many new research opportunities to explore combinations that have not been considered-perhaps for good reasons. The most common models of BRNs in literature involve differential equations, Markov processes, mass action kinetics, and state space representations whereas the most common tasks are the parameter inference and model identification. The most common methods in literature are Bayesian analysis, Monte Carlo sampling strategies, and model fitting to data using evolutionary algorithms. The new research problems which cannot be directly deduced from the text mining data are also discussed.
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Affiliation(s)
- Pavel Loskot
- College of Engineering, Swansea University, Swansea, United Kingdom
| | - Komlan Atitey
- College of Engineering, Swansea University, Swansea, United Kingdom
| | - Lyudmila Mihaylova
- Department of Automatic Control and Systems Engineering, University of Sheffield, Sheffield, United Kingdom
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Pironet A, Docherty PD, Dauby PC, Chase JG, Desaive T. Practical identifiability analysis of a minimal cardiovascular system model. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2019; 171:53-65. [PMID: 28153466 DOI: 10.1016/j.cmpb.2017.01.005] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/22/2015] [Revised: 12/01/2016] [Accepted: 01/16/2017] [Indexed: 06/06/2023]
Abstract
BACKGROUND AND OBJECTIVE Parameters of mathematical models of the cardiovascular system can be used to monitor cardiovascular state, such as total stressed blood volume status, vessel elastance and resistance. To do so, the model parameters have to be estimated from data collected at the patient's bedside. This work considers a seven-parameter model of the cardiovascular system and investigates whether these parameters can be uniquely determined using indices derived from measurements of arterial and venous pressures, and stroke volume. METHODS An error vector defined the residuals between the simulated and reference values of the seven clinically available haemodynamic indices. The sensitivity of this error vector to each model parameter was analysed, as well as the collinearity between parameters. To assess practical identifiability of the model parameters, profile-likelihood curves were constructed for each parameter. RESULTS Four of the seven model parameters were found to be practically identifiable from the selected data. The remaining three parameters were practically non-identifiable. Among these non-identifiable parameters, one could be decreased as much as possible. The other two non-identifiable parameters were inversely correlated, which prevented their precise estimation. CONCLUSIONS This work presented the practical identifiability analysis of a seven-parameter cardiovascular system model, from limited clinical data. The analysis showed that three of the seven parameters were practically non-identifiable, thus limiting the use of the model as a monitoring tool. Slight changes in the time-varying function modeling cardiac contraction and use of larger values for the reference range of venous pressure made the model fully practically identifiable.
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Affiliation(s)
- Antoine Pironet
- GIGA-In Silico Medicine, University of Liège, B5a, Quartier Agora, Allée du 6 août, 19, 4000 Liège, Belgium.
| | - Paul D Docherty
- Department of Mechanical Engineering, University of Canterbury, Private Bag 4800, Christchurch 8140, New Zealand
| | - Pierre C Dauby
- GIGA-In Silico Medicine, University of Liège, B5a, Quartier Agora, Allée du 6 août, 19, 4000 Liège, Belgium
| | - J Geoffrey Chase
- Department of Mechanical Engineering, University of Canterbury, Private Bag 4800, Christchurch 8140, New Zealand
| | - Thomas Desaive
- GIGA-In Silico Medicine, University of Liège, B5a, Quartier Agora, Allée du 6 août, 19, 4000 Liège, Belgium
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Othman NA, Docherty PD, Krebs JD, Bell DA, Chase JG. The Need to Calculate Target Glucose Levels When Measuring Changes in Insulin Sensitivity During Interventions for Individuals With Type 2 Diabetes. J Diabetes Sci Technol 2018; 12:665-672. [PMID: 29295634 PMCID: PMC6154237 DOI: 10.1177/1932296817750402] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
BACKGROUND Physiological models that are used with dynamic test data to assess insulin sensitivity (SI) assume that the metabolic target glucose concentration ( GTARGET) is equal to fasting glucose concentration ( G0). However, recent research has implied that irregularities in G0 in diabetes may cause erroneous SI values. This study quantifies the magnitude of these errors. METHODS A clinically validated insulin/glucose model was used to calculate SI with the standard fasting assumption (SFA) G0 = GTARGET. Then GTARGET was treated as a variable in a second analysis (VGT). The outcomes were contrasted across twelve participants with established type 2 diabetes mellitus that were recruited to take part in a 24-week dietary intervention. Participants underwent three insulin-modified intravenous glucose tolerance tests (IM-IVGTT) at 0, 12, and 24 weeks. RESULTS SIVGT had a median value of 3.36×10-4 L·mU-1·min-1 (IQR: 2.30 - 4.95×10-4) and were significantly lower ( P < .05) than the median SISFA (6.38×10-4 L·mU-1·min-1, IQR: 4.87 - 9.39×10-4). The VGT approach generally yielded lower SI values in line with expected participant physiology and more effectively tracked changes in participant state over the 24-week trial. Calculated GTARGET values were significantly lower than G0 values (median GTARGET = 5.48 vs G0 = 7.16 mmol·L-1 P < .001) and were notably higher in individuals with longer term diabetes. CONCLUSIONS Typical modeling approaches can overestimate SI when GTARGET does not equal G0. Hence, calculating GTARGET may enable more precise SI measurements in individuals with type 2 diabetes, and could imply a dysfunction in diabetic metabolism.
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Affiliation(s)
- Nor Azlan Othman
- Faculty of Electrical Engineering,
Universiti Teknologi MARA (UiTM), Permatang Pauh, Penang, Malaysia
| | - Paul D. Docherty
- Department of Mechanical Engineering,
Centre for Bio-Engineering, University of Canterbury, Christchurch, New
Zealand
- Paul D. Docherty, BE (hons), PhD, Department
of Mechanical Engineering, University of Canterbury, Private bag 4800,
Christchurch 8140, New Zealand.
| | - Jeremy D. Krebs
- Department of Medicine, University of
Otago, Wellington, New Zealand
| | - Damon A. Bell
- School of Medicine and Pharmacology
Royal Perth Hospital Unit, The University of Western Australia, Perth, Western
Australia, Australia
| | - J. Geoffrey Chase
- Department of Mechanical Engineering,
Centre for Bio-Engineering, University of Canterbury, Christchurch, New
Zealand
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Evaluation of pharmacokinetic model designs for subcutaneous infusion of insulin aspart. J Pharmacokinet Pharmacodyn 2017; 44:477-489. [DOI: 10.1007/s10928-017-9535-z] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2017] [Accepted: 08/11/2017] [Indexed: 10/19/2022]
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Docherty P, Geoghegan P, Huetter L, Jermy M, Sellier M. Regressive cross-correlation of pressure signals in the region of stenosis: Insights from particle image velocimetry experimentation. Biomed Signal Process Control 2017. [DOI: 10.1016/j.bspc.2016.09.025] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
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Abstract
Quantitative Systems Pharmacology (QSP) is receiving increased attention. As the momentum builds and the expectations grow it is important to (re)assess and formalize the basic concepts and approaches. In this short review, I argue that QSP, in addition to enabling the rational integration of data and development of complex models, maybe more importantly, provides the foundations for developing an integrated framework for the assessment of drugs and their impact on disease within a broader context expanding the envelope to account in great detail for physiology, environment and prior history. I articulate some of the critical enablers, major obstacles and exciting opportunities manifesting themselves along the way. Charting such overarching themes will enable practitioners to identify major and defining factors as the field progressively moves towards personalized and precision health care delivery.
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Affiliation(s)
- Ioannis P Androulakis
- Biomedical Engineering Department, Chemical & Biochemical Engineering Department, Rutgers University, Piscataway, NJ 08854
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